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Updated: Nov 15, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data
Claudia Cava1, Soudabeh Sabetian2, Isabella Castiglioni3
1Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, Segrate, 20090 Milan, Italy.
Abstract:
The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein-protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug-protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.
Insights
This study introduces a computational method to personalize cancer therapy by analyzing tumor heterogeneity. It identifies key protein targets for tailored drug combinations in basal breast cancer patients.
Area of Science:
- Oncology
- Computational Biology
- Bioinformatics
Background:
- Personalized medicine is crucial for cancer treatment.
- Tumor heterogeneity presents a significant challenge to developing effective patient-specific therapies.
- Existing drugs often fail due to intra-tumor variations.
Purpose of the Study:
- To develop a computational approach for personalized cancer therapy.
- To address the challenge of intra-tumor heterogeneity in basal breast cancer.
- To identify optimal drug combinations for individual patients.
Main Methods:
- Integrated analysis of copy number alteration, gene expression, and protein interaction networks from 73 basal breast cancer samples.
- Survival analysis to identify 2509 prognostic genes with copy number alterations.
- Construction of a protein-protein interaction network to characterize patients by seven altered hub proteins.
Main Results:
- Identified 2509 prognostic genes associated with copy number alterations.
- Characterized each of the 73 basal breast cancer patients using a unique combination of seven altered hub proteins.
- Suggested optimal combination therapies for individual patients based on drug-protein interactions.
- Validated known cancer genes and proposed novel drug targets.
Conclusions:
- Presented a novel computational approach for personalized cancer therapy.
- Demonstrated a method to overcome intra-tumor heterogeneity in basal breast cancer.
- Paved the way for more effective, patient-specific cancer treatment strategies.
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